A Genetic Algorithm Analysis Towards Optimization Solutions

Tabassum, Mujahid; Mathew, K.

doi:10.17781/p001091

Cited by 77 publications

(58 citation statements)

References 6 publications

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“…In order to examine the complex relationships among clothing choice, service experience, the perceived brand image, and customers' expectations, a conceptual model was developed and a genetic algorithm (GA) approach was employed. GA was used for this study because (1) it is one of the most powerful unbiased optimisation techniques -using probabilistic rules instead of deterministic rules (Tabassum & Mathew, 2014); (2) it is useful if prior knowledge is limited (e.g. if developing hypotheses seems to be premature); and (3) it is capable of generating efficient solutions for multidimensional problems (e.g.…”

Section: Service Brand Image and Customers' Attirementioning

confidence: 99%

Service brand and customer attire: a genetic algorithm approach

Chang

Rahman

Hsu

et al. 2015

International Journal of Fashion Design, Technology and Educati

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The service industry has given increasing attention to the significance of brand building. In order to understand and examine the relationships among service experience, perceived brand image and clothing choice, a literature-based conceptual model was developed and genetic algorithm approach was employed. Empirical data were collected from two steakhouses in Taiwan. The results of this study reported how clothing could be used and perceived in a social setting -restaurant. Our findings offer new insights and implications to service brand managers and frontline service employees on the areas of customer perceptions and expectations towards the brand through their clothing choices. In addition, practical strategies for constructing brand personality and customer service were recommended.

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Section: Service Brand Image and Customers' Attirementioning

confidence: 99%

Service brand and customer attire: a genetic algorithm approach

Chang

Rahman

Hsu

et al. 2015

International Journal of Fashion Design, Technology and Educati

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“…Past collaboration experience among the surgical providers is another influencing factor which we considered it in our proposed system. We used metaheuristic approach due to several reasons: 1) The ability of the program to quickly provide a set of multiple local optima which brings more flexibility by listing alternative teams, 2) its high performance on noisy data, 3) ease of distribution and parallelization, 4) simplicity and ease of interpretation [28,58]. The results show the usability and the great potential of DisTeam in forming surgical teams.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…The availability of large-scale datasets and the complex nature of real world problems have resulted in development of various intelligent and heuristic approaches in different domains [28]. Genetic algorithm (GA), developed by John Holland at the University of Michigan in 1960’s and 1970’s [29], is a search metaheuristic which belongs to the larger class of evolutionary algorithms and is widely used in the field of artificial intelligence.…”

Section: Related Workmentioning

confidence: 99%

DisTeam: A decision support tool for surgical team selection

Ebadi

Tighe

Zhang

et al. 2017

Artificial Intelligence in Medicine

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Objective Surgical service providers play a crucial role in the healthcare system. Amongst all the influencing factors, surgical team selection might affect the patients’ outcome significantly. The performance of a surgical team not only can depend on the individual members, but it can also depend on the synergy among team members, and could possibly influence patient outcome such as surgical complications. In this paper, we propose a tool for facilitating decision making in surgical team selection based on considering history of the surgical team, as well as the specific characteristics of each patient. Methods DisTeam (a decision support tool for surgical team selection) is a metaheuristic framework for objective evaluation of surgical teams and finding the optimal team for a given patient, in terms of number of complications. It identifies a ranked list of surgical teams personalized for each patient, based on prior performance of the surgical teams. DisTeam takes into account the surgical complications associated with teams and their members, their teamwork history, as well as patient’s specific characteristics such as age, body mass index (BMI) and Charlson comorbidity index score. Results We tested DisTeam using intra-operative data from 6065 unique orthopedic surgery cases. Our results suggest high effectiveness of the proposed system in a health-care setting. The proposed framework converges quickly to the optimal solution and provides two sets of answers: a) The best surgical team over all the generations, and b) The best population which consists of different teams that can be used as an alternative solution. This increases the flexibility of the system as a complementary decision support tool. Conclusion DisTeam is a decision support tool for assisting in surgical team selection. It can facilitate the job of scheduling personnel in the hospital which involves an overwhelming number of factors pertaining to patients, individual team members, and team dynamics and can be used to compose patient-personalized surgical teams with minimum (potential) surgical complications.

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“…It can be seen that the count correct method obtains its best result after only a few iterations but then never seems to make any further necessary for a GA search and so can be used in many diverse applications [26]. Here a GA will be applied to the four line segment method of estimating the fuzzy measure.…”

Section: Hill Climbingmentioning

confidence: 99%

Heuristic Methods to Identify Fuzzy Measures Applied to Choquet Integral Classification of Breast Cancer Data

Adams¹

2015

IJDIWC

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When the search space for optimisation or classification problems gets large heuristics and soft computing are often used to find the needed parameters. The Choquet integral methods needs one parameter for each subset of the condition attributes because these sets form a lattice on which the fuzzy measure needed for integration is defined. In this paper the frequency of how many data from each class pass through each set in the lattice provides information that is then used to construct the fuzzy measure. Several heuristics based on this idea are tested upon the well-known Wisconsin breast cancer data set. The heuristics prove to be as successful, or more so, than a range of other methods quoted in the literature when 10-fold cross validation is performed.In order for the Choquet to separate classes after integration a boundary value has to be calculated.Remarkably, it is found that switching the way to view success from number of records correctly classified to the distance of the wrongly classified record from the boundary leads to a hill climbing approach that can reclassify the data with a 100% success rate.

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A Genetic Algorithm Analysis Towards Optimization Solutions

Cited by 77 publications

References 6 publications

Service brand and customer attire: a genetic algorithm approach

Service brand and customer attire: a genetic algorithm approach

DisTeam: A decision support tool for surgical team selection

Heuristic Methods to Identify Fuzzy Measures Applied to Choquet Integral Classification of Breast Cancer Data

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